Overview

Dataset statistics

Number of variables18
Number of observations1958
Missing cells10323
Missing cells (%)29.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory264.0 KiB
Average record size in memory138.1 B

Variable types

NUM15
CAT2
BOOL1

Warnings

country has a high cardinality: 196 distinct values High cardinality
production_chickens_tonnes is highly correlated with stocks_chickens_thsdhd and 2 other fieldsHigh correlation
stocks_chickens_thsdhd is highly correlated with production_chickens_tonnes and 2 other fieldsHigh correlation
slaughtered_chickens_thsdhd is highly correlated with stocks_chickens_thsdhd and 2 other fieldsHigh correlation
stocks_chickens_thsdhd_lag is highly correlated with stocks_chickens_thsdhd and 2 other fieldsHigh correlation
producerprice_chickens_live_lcupertonne is highly correlated with producerprice_chickens_carcass_lcupertonne and 2 other fieldsHigh correlation
producerprice_chickens_carcass_lcupertonne is highly correlated with producerprice_chickens_live_lcupertonne and 2 other fieldsHigh correlation
producerprice_chickens_carcass_slcpertonne is highly correlated with producerprice_chickens_carcass_lcupertonne and 2 other fieldsHigh correlation
producerprice_chickens_live_slcpertonne is highly correlated with producerprice_chickens_carcass_lcupertonne and 2 other fieldsHigh correlation
stocks_chickens_thsdhd has 35 (1.8%) missing values Missing
stocks_chickens_thsdhd_lag has 214 (10.9%) missing values Missing
stockschange_chickens_thsdhd has 232 (11.8%) missing values Missing
producerprice_chickens_carcass_lcupertonne has 1535 (78.4%) missing values Missing
producerprice_chickens_live_lcupertonne has 1290 (65.9%) missing values Missing
producerprice_chickens_carcass_slcpertonne has 1535 (78.4%) missing values Missing
producerprice_chickens_live_slcpertonne has 1290 (65.9%) missing values Missing
producerprice_chickens_carcass_usdpertonne has 1540 (78.7%) missing values Missing
producerprice_chickens_live_usdpertonne has 1297 (66.2%) missing values Missing
producerprice_chickens_carcass_index has 689 (35.2%) missing values Missing
producerprice_chickens_live_index has 656 (33.5%) missing values Missing
stockschange_chickens_thsdhd is highly skewed (γ1 = 21.45703561) Skewed
country is uniformly distributed Uniform
stockschange_chickens_thsdhd has 151 (7.7%) zeros Zeros

Reproduction

Analysis started2022-04-08 16:38:24.332690
Analysis finished2022-04-08 16:39:04.873103
Duration40.54 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

country
Categorical

HIGH CARDINALITY
UNIFORM

Distinct196
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
Poland
 
10
Eswatini
 
10
Tonga
 
10
Armenia
 
10
Mexico
 
10
Other values (191)
1908 
ValueCountFrequency (%) 
Poland100.5%
 
Eswatini100.5%
 
Tonga100.5%
 
Armenia100.5%
 
Mexico100.5%
 
Jordan100.5%
 
Botswana100.5%
 
United States of America100.5%
 
Azerbaijan100.5%
 
Oman100.5%
 
Other values (186)185894.9%
 
2022-04-08T09:39:04.989010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-04-08T09:39:05.173987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length52
Median length7.5
Mean length10.1195097
Min length4

year
Real number (ℝ≥0)

Distinct10
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.504597
Minimum2011
Maximum2020
Zeros0
Zeros (%)0.0%
Memory size15.3 KiB
2022-04-08T09:39:05.305286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011
Q12013
median2016
Q32018
95-th percentile2020
Maximum2020
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.870876317
Coefficient of variation (CV)0.001424395817
Kurtosis-1.223366815
Mean2015.504597
Median Absolute Deviation (MAD)2
Skewness-0.0008683544075
Sum3946358
Variance8.241930828
MonotocityNot monotonic
2022-04-08T09:39:05.421153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
201219610.0%
 
201319610.0%
 
201419610.0%
 
201519610.0%
 
201619610.0%
 
201719610.0%
 
201819610.0%
 
201919610.0%
 
202019610.0%
 
20111949.9%
 
ValueCountFrequency (%) 
20111949.9%
 
201219610.0%
 
201319610.0%
 
201419610.0%
 
201519610.0%
 
ValueCountFrequency (%) 
202019610.0%
 
201919610.0%
 
201819610.0%
 
201719610.0%
 
201619610.0%
 

stocks_chickens_thsdhd
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1663
Distinct (%)86.5%
Missing35
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean179396.2912
Minimum5
Maximum9222100
Zeros0
Zeros (%)0.0%
Memory size15.3 KiB
2022-04-08T09:39:05.590559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile191
Q13600
median18198
Q364486
95-th percentile409150.7
Maximum9222100
Range9222095
Interquartile range (IQR)60886

Descriptive statistics

Standard deviation828276.2365
Coefficient of variation (CV)4.617019844
Kurtosis69.96891515
Mean179396.2912
Median Absolute Deviation (MAD)17503
Skewness7.969929737
Sum344979068
Variance6.86041524e+11
MonotocityNot monotonic
2022-04-08T09:39:05.753330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5201.0%
 
200100.5%
 
15000100.5%
 
100080.4%
 
130080.4%
 
1870.4%
 
27070.4%
 
3400060.3%
 
25060.3%
 
13800060.3%
 
Other values (1653)183593.7%
 
(Missing)351.8%
 
ValueCountFrequency (%) 
5201.0%
 
1740.2%
 
1870.4%
 
1950.3%
 
2040.2%
 
ValueCountFrequency (%) 
922210010.1%
 
917720010.1%
 
903830010.1%
 
891380010.1%
 
877670010.1%
 

yield_chickens_tenthgramperhd
Real number (ℝ≥0)

Distinct1360
Distinct (%)69.6%
Missing4
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean13368.87922
Minimum5493
Maximum30002
Zeros0
Zeros (%)0.0%
Memory size15.3 KiB
2022-04-08T09:39:05.938313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5493
5-th percentile7833.55
Q19001
median12650.5
Q316630.75
95-th percentile21654.2
Maximum30002
Range24509
Interquartile range (IQR)7629.75

Descriptive statistics

Standard deviation4960.148314
Coefficient of variation (CV)0.3710220005
Kurtosis0.8568602339
Mean13368.87922
Median Absolute Deviation (MAD)3690.5
Skewness0.9161210882
Sum26122790
Variance24603071.29
MonotocityNot monotonic
2022-04-08T09:39:06.118820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
80001497.6%
 
100001326.7%
 
9000442.2%
 
12000140.7%
 
5500120.6%
 
30000110.6%
 
11500110.6%
 
8333110.6%
 
11000100.5%
 
798090.5%
 
Other values (1350)155179.2%
 
ValueCountFrequency (%) 
549310.1%
 
549710.1%
 
5500120.6%
 
555610.1%
 
562510.1%
 
ValueCountFrequency (%) 
3000220.1%
 
3000110.1%
 
30000110.6%
 
2999910.1%
 
2999820.1%
 

production_chickens_tonnes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1800
Distinct (%)92.0%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean611882.0133
Minimum0
Maximum20490251
Zeros4
Zeros (%)0.2%
Memory size15.3 KiB
2022-04-08T09:39:06.303648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile244.75
Q16769
median53111
Q3235751.75
95-th percentile2071032
Maximum20490251
Range20490251
Interquartile range (IQR)228982.75

Descriptive statistics

Standard deviation2155959.128
Coefficient of variation (CV)3.523488321
Kurtosis41.89344256
Mean611882.0133
Median Absolute Deviation (MAD)52180.5
Skewness6.259686244
Sum1196841218
Variance4.648159761e+12
MonotocityNot monotonic
2022-04-08T09:39:06.482032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5100.5%
 
4100.5%
 
1960.3%
 
360060.3%
 
2060.3%
 
630050.3%
 
040.2%
 
2240.2%
 
28340.2%
 
2000040.2%
 
Other values (1790)189796.9%
 
ValueCountFrequency (%) 
040.2%
 
4100.5%
 
5100.5%
 
910.1%
 
1310.1%
 
ValueCountFrequency (%) 
2049025110.1%
 
2017285410.1%
 
1956804210.1%
 
1914057010.1%
 
1870846510.1%
 

slaughtered_chickens_thsdhd
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1764
Distinct (%)90.3%
Missing4
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean383861.3398
Minimum0
Maximum11050548
Zeros4
Zeros (%)0.2%
Memory size15.3 KiB
2022-04-08T09:39:06.682637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile202
Q16787.25
median43000
Q3175528.25
95-th percentile1246208.25
Maximum11050548
Range11050548
Interquartile range (IQR)168741

Descriptive statistics

Standard deviation1279072.125
Coefficient of variation (CV)3.332120202
Kurtosis38.6097756
Mean383861.3398
Median Absolute Deviation (MAD)41400
Skewness6.014484843
Sum750065058
Variance1.636025502e+12
MonotocityNot monotonic
2022-04-08T09:39:06.883337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
24110.6%
 
5100.5%
 
6100.5%
 
55070.4%
 
700070.4%
 
3000050.3%
 
450040.2%
 
2540.2%
 
19940.2%
 
8380040.2%
 
Other values (1754)188896.4%
 
ValueCountFrequency (%) 
040.2%
 
5100.5%
 
6100.5%
 
1330.2%
 
1620.1%
 
ValueCountFrequency (%) 
1105054810.1%
 
1080114810.1%
 
1066078110.1%
 
1042837110.1%
 
1029764610.1%
 
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
False
1828 
True
 
130
ValueCountFrequency (%) 
False182893.4%
 
True1306.6%
 
2022-04-08T09:39:07.068259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

stocks_chickens_thsdhd_lag
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1498
Distinct (%)85.9%
Missing214
Missing (%)10.9%
Infinite0
Infinite (%)0.0%
Mean176459.9174
Minimum5
Maximum9177200
Zeros0
Zeros (%)0.0%
Memory size15.3 KiB
2022-04-08T09:39:07.168541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile191
Q13585.5
median18000
Q363275
95-th percentile391439.55
Maximum9177200
Range9177195
Interquartile range (IQR)59689.5

Descriptive statistics

Standard deviation820774.9609
Coefficient of variation (CV)4.651339368
Kurtosis70.77353041
Mean176459.9174
Median Absolute Deviation (MAD)17346.5
Skewness8.025246915
Sum307746096
Variance6.736715364e+11
MonotocityNot monotonic
2022-04-08T09:39:07.337914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5180.9%
 
1500090.5%
 
20090.5%
 
130080.4%
 
100070.4%
 
27070.4%
 
1860.3%
 
13800060.3%
 
3400060.3%
 
360060.3%
 
Other values (1488)166284.9%
 
(Missing)21410.9%
 
ValueCountFrequency (%) 
5180.9%
 
1740.2%
 
1860.3%
 
1940.2%
 
2040.2%
 
ValueCountFrequency (%) 
917720010.1%
 
903830010.1%
 
891380010.1%
 
877670010.1%
 
868870010.1%
 

stockschange_chickens_thsdhd
Real number (ℝ)

MISSING
SKEWED
ZEROS

Distinct1114
Distinct (%)64.5%
Missing232
Missing (%)11.8%
Infinite0
Infinite (%)0.0%
Mean3832.209733
Minimum-308347
Maximum1393102
Zeros151
Zeros (%)7.7%
Memory size15.3 KiB
2022-04-08T09:39:07.522758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-308347
5-th percentile-2386.5
Q10
median100
Q31414.25
95-th percentile14674.5
Maximum1393102
Range1701449
Interquartile range (IQR)1414.25

Descriptive statistics

Standard deviation41972.23025
Coefficient of variation (CV)10.95248777
Kurtosis705.8181305
Mean3832.209733
Median Absolute Deviation (MAD)413
Skewness21.45703561
Sum6614394
Variance1761668112
MonotocityNot monotonic
2022-04-08T09:39:07.692035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01517.7%
 
1281.4%
 
500231.2%
 
100170.9%
 
50150.8%
 
2150.8%
 
10120.6%
 
-1120.6%
 
5100.5%
 
1190.5%
 
Other values (1104)143473.2%
 
(Missing)23211.8%
 
ValueCountFrequency (%) 
-30834710.1%
 
-30476310.1%
 
-20253810.1%
 
-20200010.1%
 
-17540610.1%
 
ValueCountFrequency (%) 
139310210.1%
 
32212710.1%
 
31587310.1%
 
21943710.1%
 
20767010.1%
 

producerprice_chickens_carcass_lcupertonne
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct398
Distinct (%)94.1%
Missing1535
Missing (%)78.4%
Infinite0
Infinite (%)0.0%
Mean2887876.288
Minimum1032
Maximum98955653
Zeros0
Zeros (%)0.0%
Memory size15.3 KiB
2022-04-08T09:39:07.908191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1032
5-th percentile1318.1
Q14028.5
median17698
Q3250449
95-th percentile5129833.1
Maximum98955653
Range98954621
Interquartile range (IQR)246420.5

Descriptive statistics

Standard deviation13863304.8
Coefficient of variation (CV)4.800518934
Kurtosis35.27458702
Mean2887876.288
Median Absolute Deviation (MAD)16019
Skewness5.941415946
Sum1221571670
Variance1.9219122e+14
MonotocityNot monotonic
2022-04-08T09:39:08.124386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
600050.3%
 
1267740.2%
 
1166030.2%
 
211320.1%
 
250000020.1%
 
131320.1%
 
151020.1%
 
1541920.1%
 
71050220.1%
 
43613720.1%
 
Other values (388)39720.3%
 
(Missing)153578.4%
 
ValueCountFrequency (%) 
103210.1%
 
105210.1%
 
110710.1%
 
112210.1%
 
115010.1%
 
ValueCountFrequency (%) 
9895565310.1%
 
9651230910.1%
 
9576000010.1%
 
9452800010.1%
 
9444500010.1%
 

producerprice_chickens_live_lcupertonne
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct617
Distinct (%)92.4%
Missing1290
Missing (%)65.9%
Infinite0
Infinite (%)0.0%
Mean1201356.41
Minimum478
Maximum42588000
Zeros0
Zeros (%)0.0%
Memory size15.3 KiB
2022-04-08T09:39:08.309259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum478
5-th percentile860.7
Q11857
median8159
Q394078.75
95-th percentile2971781.95
Maximum42588000
Range42587522
Interquartile range (IQR)92221.75

Descriptive statistics

Standard deviation5244542.578
Coefficient of variation (CV)4.365517621
Kurtosis28.435659
Mean1201356.41
Median Absolute Deviation (MAD)7227
Skewness5.311953918
Sum802506082
Variance2.750522685e+13
MonotocityNot monotonic
2022-04-08T09:39:08.541080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
160070.4%
 
107760.3%
 
903960.3%
 
333340.2%
 
350030.2%
 
506030.2%
 
191176530.2%
 
400030.2%
 
93220.1%
 
131020.1%
 
Other values (607)62932.1%
 
(Missing)129065.9%
 
ValueCountFrequency (%) 
47810.1%
 
52610.1%
 
53510.1%
 
54110.1%
 
77210.1%
 
ValueCountFrequency (%) 
4258800010.1%
 
3778000010.1%
 
3586933310.1%
 
3451200010.1%
 
3356533310.1%
 

producerprice_chickens_carcass_slcpertonne
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct397
Distinct (%)93.9%
Missing1535
Missing (%)78.4%
Infinite0
Infinite (%)0.0%
Mean2809386.832
Minimum1032
Maximum98955653
Zeros0
Zeros (%)0.0%
Memory size15.3 KiB
2022-04-08T09:39:08.726079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1032
5-th percentile1318.1
Q12838
median17042
Q3249500
95-th percentile4987058.4
Maximum98955653
Range98954621
Interquartile range (IQR)246662

Descriptive statistics

Standard deviation13832228.72
Coefficient of variation (CV)4.923575693
Kurtosis35.75221365
Mean2809386.832
Median Absolute Deviation (MAD)15363
Skewness5.993882377
Sum1188370630
Variance1.913305513e+14
MonotocityNot monotonic
2022-04-08T09:39:08.911065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
600050.3%
 
1267740.2%
 
1166030.2%
 
25000020.1%
 
250000020.1%
 
131320.1%
 
2000020.1%
 
933820.1%
 
251820.1%
 
151020.1%
 
Other values (387)39720.3%
 
(Missing)153578.4%
 
ValueCountFrequency (%) 
103210.1%
 
105210.1%
 
110710.1%
 
112210.1%
 
115010.1%
 
ValueCountFrequency (%) 
9895565310.1%
 
9651230910.1%
 
9576000010.1%
 
9452800010.1%
 
9444500010.1%
 

producerprice_chickens_live_slcpertonne
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct612
Distinct (%)91.6%
Missing1290
Missing (%)65.9%
Infinite0
Infinite (%)0.0%
Mean1096886.933
Minimum478
Maximum42588000
Zeros0
Zeros (%)0.0%
Memory size15.3 KiB
2022-04-08T09:39:09.111691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum478
5-th percentile862.7
Q11722.25
median7523.5
Q387906.25
95-th percentile2800130.05
Maximum42588000
Range42587522
Interquartile range (IQR)86184

Descriptive statistics

Standard deviation5141798.49
Coefficient of variation (CV)4.687628539
Kurtosis31.25289844
Mean1096886.933
Median Absolute Deviation (MAD)6587.5
Skewness5.603310482
Sum732720471
Variance2.643809171e+13
MonotocityNot monotonic
2022-04-08T09:39:09.274459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
160070.4%
 
107760.3%
 
903960.3%
 
333340.2%
 
400030.2%
 
92630.2%
 
91330.2%
 
350030.2%
 
191176530.2%
 
506030.2%
 
Other values (602)62732.0%
 
(Missing)129065.9%
 
ValueCountFrequency (%) 
47810.1%
 
52610.1%
 
53510.1%
 
54110.1%
 
55610.1%
 
ValueCountFrequency (%) 
4258800010.1%
 
3778000010.1%
 
3586933310.1%
 
3451200010.1%
 
3356533310.1%
 

producerprice_chickens_carcass_usdpertonne
Real number (ℝ≥0)

MISSING

Distinct387
Distinct (%)92.6%
Missing1540
Missing (%)78.7%
Infinite0
Infinite (%)0.0%
Mean2834.574163
Minimum950
Maximum7794
Zeros0
Zeros (%)0.0%
Memory size15.3 KiB
2022-04-08T09:39:09.458986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum950
5-th percentile1316.65
Q11857.5
median2472
Q33502.25
95-th percentile5367.95
Maximum7794
Range6844
Interquartile range (IQR)1644.75

Descriptive statistics

Standard deviation1280.544839
Coefficient of variation (CV)0.4517591587
Kurtosis0.5154892801
Mean2834.574163
Median Absolute Deviation (MAD)767
Skewness0.9681768281
Sum1184852
Variance1639795.084
MonotocityNot monotonic
2022-04-08T09:39:09.675130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
469540.2%
 
206830.2%
 
431930.2%
 
286030.2%
 
184730.2%
 
346620.1%
 
425920.1%
 
201120.1%
 
462120.1%
 
134220.1%
 
Other values (377)39220.0%
 
(Missing)154078.7%
 
ValueCountFrequency (%) 
95010.1%
 
96810.1%
 
102810.1%
 
103910.1%
 
107510.1%
 
ValueCountFrequency (%) 
779410.1%
 
720410.1%
 
716910.1%
 
642410.1%
 
632210.1%
 

producerprice_chickens_live_usdpertonne
Real number (ℝ≥0)

MISSING

Distinct585
Distinct (%)88.5%
Missing1297
Missing (%)66.2%
Infinite0
Infinite (%)0.0%
Mean1923.576399
Minimum445
Maximum7859
Zeros0
Zeros (%)0.0%
Memory size15.3 KiB
2022-04-08T09:39:09.860125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum445
5-th percentile888
Q11166
median1517
Q32330
95-th percentile4387
Maximum7859
Range7414
Interquartile range (IQR)1164

Descriptive statistics

Standard deviation1172.68468
Coefficient of variation (CV)0.609637694
Kurtosis4.201603597
Mean1923.576399
Median Absolute Deviation (MAD)469
Skewness1.910726961
Sum1271484
Variance1375189.36
MonotocityNot monotonic
2022-04-08T09:39:10.029275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
334860.3%
 
118740.2%
 
96430.2%
 
119430.2%
 
187430.2%
 
156830.2%
 
131330.2%
 
400030.2%
 
120530.2%
 
93030.2%
 
Other values (575)62732.0%
 
(Missing)129766.2%
 
ValueCountFrequency (%) 
44510.1%
 
54710.1%
 
54910.1%
 
55010.1%
 
61910.1%
 
ValueCountFrequency (%) 
785910.1%
 
735310.1%
 
698710.1%
 
692910.1%
 
678310.1%
 

producerprice_chickens_carcass_index
Real number (ℝ≥0)

MISSING

Distinct124
Distinct (%)9.8%
Missing689
Missing (%)35.2%
Infinite0
Infinite (%)0.0%
Mean100.7604413
Minimum17
Maximum372
Zeros0
Zeros (%)0.0%
Memory size15.3 KiB
2022-04-08T09:39:10.215563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile73
Q193
median100
Q3107
95-th percentile130
Maximum372
Range355
Interquartile range (IQR)14

Descriptive statistics

Standard deviation20.76508182
Coefficient of variation (CV)0.2060836729
Kurtosis28.25481394
Mean100.7604413
Median Absolute Deviation (MAD)7
Skewness2.689931221
Sum127865
Variance431.1886231
MonotocityNot monotonic
2022-04-08T09:39:10.376892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
100784.0%
 
99593.0%
 
98572.9%
 
101562.9%
 
96492.5%
 
102462.3%
 
94452.3%
 
97442.2%
 
104432.2%
 
95412.1%
 
Other values (114)75138.4%
 
(Missing)68935.2%
 
ValueCountFrequency (%) 
1710.1%
 
2910.1%
 
3210.1%
 
3520.1%
 
3610.1%
 
ValueCountFrequency (%) 
37210.1%
 
23110.1%
 
22710.1%
 
20620.1%
 
19510.1%
 

producerprice_chickens_live_index
Real number (ℝ≥0)

MISSING

Distinct128
Distinct (%)9.8%
Missing656
Missing (%)33.5%
Infinite0
Infinite (%)0.0%
Mean100.5537634
Minimum31
Maximum440
Zeros0
Zeros (%)0.0%
Memory size15.3 KiB
2022-04-08T09:39:10.577443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile73
Q194
median100
Q3106
95-th percentile127
Maximum440
Range409
Interquartile range (IQR)12

Descriptive statistics

Standard deviation21.19035893
Coefficient of variation (CV)0.210736607
Kurtosis64.08453351
Mean100.5537634
Median Absolute Deviation (MAD)6
Skewness4.774080083
Sum130921
Variance449.0313117
MonotocityNot monotonic
2022-04-08T09:39:10.731183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
100874.4%
 
98713.6%
 
101653.3%
 
99583.0%
 
96562.9%
 
97522.7%
 
105482.5%
 
104452.3%
 
95412.1%
 
94412.1%
 
Other values (118)73837.7%
 
(Missing)65633.5%
 
ValueCountFrequency (%) 
3110.1%
 
3220.1%
 
3410.1%
 
3510.1%
 
3820.1%
 
ValueCountFrequency (%) 
44010.1%
 
31810.1%
 
26810.1%
 
23510.1%
 
19610.1%
 

_merge_prodprice
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
both
1410 
left_only
548 
ValueCountFrequency (%) 
both141072.0%
 
left_only54828.0%
 
2022-04-08T09:39:10.916122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-04-08T09:39:11.000755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:11.101074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length4
Mean length5.39938713
Min length4

Interactions

2022-04-08T09:38:25.375269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:25.535575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:25.704896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:25.905438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:26.090383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:26.237573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:26.375613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:26.522802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:26.692235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:26.855000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:27.008742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:27.171587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:27.325336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:27.472482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:27.626154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:27.795594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:27.942752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:28.127669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:28.259236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:28.412942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:28.560043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:28.713715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:28.845244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:28.998965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:29.146216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:29.300007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:29.446921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:29.616208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:29.763441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:29.901494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:30.048689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:30.186719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:30.333926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:30.465458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:30.619121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:30.766221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:30.904382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:31.035915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:31.183101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:31.321264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:31.490505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:31.653347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:31.807063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:31.938595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:32.092338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:32.239569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:32.393224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:32.555999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:32.709831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:32.888134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:33.041915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:33.795131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:33.964449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:34.118183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:34.280955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:34.450350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:34.621688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:34.793086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:34.982630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:35.143611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:35.307739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:35.458262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:35.630795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:35.778025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:35.931740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:36.132277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:36.348519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:36.480087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:36.653963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:36.823338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:36.970549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:37.124202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:37.271357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:37.425029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:37.572182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:37.725921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:37.873077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:38.020231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:38.205146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:38.358862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:38.490308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:38.675167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:38.806666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:38.975985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:39.107518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:39.261222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:39.411004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:39.565335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:39.708973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:39.846970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:40.009778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:40.210210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:40.348304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:40.495408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:40.649075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:40.811884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:41.012369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:41.166068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:41.328796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:41.482325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:41.635949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:41.783148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:41.936801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:42.068414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:42.199937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:42.353559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:42.500786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:42.654481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:42.817252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:42.986576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:43.140242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:43.287451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:43.456725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:43.635072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:43.804400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:43.973684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:44.143083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:44.305953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:44.459613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:44.622330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:44.807245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:44.960947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:45.123710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:45.277378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:45.427201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:45.609214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:45.762959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:45.925725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:46.079472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:46.248803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:46.427951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:46.644098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:46.797762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:46.967200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:47.114388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:47.295639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:47.447273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:47.626731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:47.780224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:47.940901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:48.094747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:48.296685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:48.446236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:48.646813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:48.816089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:48.978913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:49.148299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:49.317572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:49.464700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:49.634109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:49.796837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:49.966241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:50.119929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:50.282747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:50.436517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:50.637010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:50.799824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:50.969141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:51.122794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:51.285428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:51.543941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:51.815919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:51.983308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:52.137028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:52.299849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:52.469213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:52.638547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:52.785744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:52.955029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:53.102252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:53.255959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:53.404663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:53.572522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:53.741875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:53.904721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:54.074085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:54.243390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:54.406249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:54.559918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:54.722725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:54.892047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:55.039228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:55.177328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:55.324428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:55.462616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:55.647506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:55.810245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:55.948318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:56.111125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:56.264925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:56.412129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:56.565879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:56.797698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:56.951423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:57.082960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:57.230148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:57.385305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:57.515369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:57.669035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:57.816193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:57.985522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:58.132673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:58.270794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:58.471286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:58.656266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:58.819122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:58.972822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:59.135648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:59.273529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:59.423381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:59.605352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:59.759134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:38:59.921942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:00.106766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:00.260237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:00.423092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:00.576807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:00.739611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:01.742479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:01.927382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:02.096747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:02.265948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:02.413100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:02.582460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:02.745227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2022-04-08T09:39:11.248235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-08T09:39:11.618083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-08T09:39:11.965811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-08T09:39:12.320061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-08T09:39:03.067814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:03.631914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:04.086561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-04-08T09:39:04.619246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

countryyearstocks_chickens_thsdhdyield_chickens_tenthgramperhdproduction_chickens_tonnesslaughtered_chickens_thsdhdcountry_inscopestocks_chickens_thsdhd_lagstockschange_chickens_thsdhdproducerprice_chickens_carcass_lcupertonneproducerprice_chickens_live_lcupertonneproducerprice_chickens_carcass_slcpertonneproducerprice_chickens_live_slcpertonneproducerprice_chickens_carcass_usdpertonneproducerprice_chickens_live_usdpertonneproducerprice_chickens_carcass_indexproducerprice_chickens_live_index_merge_prodprice
0Afghanistan201113378.08000.025600.032000.0FalseNaNNaNNaNNaNNaNNaNNaNNaN111.0111.0both
1Afghanistan201213212.08000.024800.031000.0False13378.0-166.0NaNNaNNaNNaNNaNNaN113.0113.0both
2Afghanistan201312053.08000.026400.033000.0False13212.0-1159.0NaNNaNNaNNaNNaNNaN109.0109.0both
3Afghanistan201411098.08000.024809.031012.0False12053.0-955.0NaNNaNNaNNaNNaNNaN105.0105.0both
4Afghanistan201511863.08000.024559.030698.0False11098.0765.0NaNNaNNaNNaNNaNNaN101.0101.0both
5Afghanistan201611899.08000.024298.030372.0False11863.036.0NaNNaNNaNNaNNaNNaN93.093.0both
6Afghanistan201713573.08000.027694.034618.0False11899.01674.0NaNNaNNaNNaNNaNNaN90.090.0both
7Afghanistan201814388.08000.029336.036670.0False13573.0815.0NaNNaNNaNNaNNaNNaN85.085.0both
8Afghanistan201913760.08000.028033.035041.0False14388.0-628.0NaNNaNNaNNaNNaNNaN88.088.0both
9Afghanistan202013724.08000.027938.034923.0False13760.0-36.0NaNNaNNaNNaNNaNNaNNaNNaNleft_only

Last rows

countryyearstocks_chickens_thsdhdyield_chickens_tenthgramperhdproduction_chickens_tonnesslaughtered_chickens_thsdhdcountry_inscopestocks_chickens_thsdhd_lagstockschange_chickens_thsdhdproducerprice_chickens_carcass_lcupertonneproducerprice_chickens_live_lcupertonneproducerprice_chickens_carcass_slcpertonneproducerprice_chickens_live_slcpertonneproducerprice_chickens_carcass_usdpertonneproducerprice_chickens_live_usdpertonneproducerprice_chickens_carcass_indexproducerprice_chickens_live_index_merge_prodprice
1948Zimbabwe201122311.011500.063250.055000.0FalseNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNleft_only
1949Zimbabwe201221050.011500.063825.055500.0False22311.0-1261.0NaNNaNNaNNaNNaNNaNNaNNaNleft_only
1950Zimbabwe201319749.011500.063825.055500.0False21050.0-1301.0NaNNaNNaNNaNNaNNaNNaNNaNleft_only
1951Zimbabwe201413311.011500.057500.050000.0False19749.0-6438.0NaN4333.0NaN4333.0NaN4333.0NaN105.0both
1952Zimbabwe20159510.011500.060950.053000.0False13311.0-3801.0NaN4000.0NaN4000.0NaN4000.0NaN97.0both
1953Zimbabwe20168000.011500.066700.058000.0False9510.0-1510.0NaN4000.0NaN4000.0NaN4000.0NaN97.0both
1954Zimbabwe20177965.011500.069000.060000.0False8000.0-35.0NaN4000.0NaN4000.0NaN4000.0NaN97.0both
1955Zimbabwe20187690.011500.065550.057000.0False7965.0-275.0NaN4667.0NaN4667.0NaN4667.0NaN114.0both
1956Zimbabwe20197083.011500.067083.058333.0False7690.0-607.0NaNNaNNaNNaNNaNNaNNaN82.0both
1957Zimbabwe20207579.011500.067211.058444.0False7083.0496.0NaNNaNNaNNaNNaNNaNNaNNaNleft_only